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ice, using a framework consisting of an ocean Large Eddy Simulation (LES) and a Discrete Element Model (DEM) of sea ice. Results from these simulations will be validated against a combination of in-situ
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feeding into the MNase-defined cistrome occupancy (MOA-seq) analysis of transcription factor binding of tomato promoter elements controlling the Zn and Fe deficiency responsive gene expression. You will
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models. As a result, your project will inform and enhance decision-making in coastal dune management. The ultimate goal of this project is to build a surrogate wind field model that can feed accurate wind
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consisting of an ocean Large Eddy Simulation (LES) and a Discrete Element Model (DEM) of sea ice. Results from these simulations will be validated against a combination of in-situ and remote sensing data from
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As a postdoctoral researcher, your primary responsibilities will be: Develop machine learning and deep learning models, with a strong focus on computer vision, for the characterisation and
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partners to make models and associated tools usable beyond the immediate research setting. You will not work on an isolated AI benchmark. ARCA predictions will be tested experimentally in greenhouse and
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partners to make models and associated tools usable beyond the immediate research setting. You will not work on an isolated AI benchmark. ARCA predictions will be tested experimentally in greenhouse and
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the content of the science they study. You will be an active member of RCNP. You will collaborate with the other postdoctoral researchers, PhD candidates and senior investigators, participate in seminars and
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status of tomatoes for human health. You will generate dedicated (single-cell) transcriptional data sets feeding into the MNase-defined cistrome occupancy (MOA-seq) analysis of transcription factor binding
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collide, condense and evaporate. Guided by the results from the field campaigns, a new Microphysics Informed Large-Eddy-Simulation (MiLES) model will be developed. The tested and verified MiLES will be